System and method for processing multiple signals

ABSTRACT

A system and a method for processing multiple signals generated by sensors processing to identify and/or monitor physiological data of an individual (for example in healthcare system) or general statement of an environment, a predetermined space (for example a room, a machine, a building) or an object (for example in smart home system, environment monitoring system, fire prevention system or the like).

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is the U.S. national phase entry under 35 U.S.C. § 371of International Application No. PCT/EP2019/056340, filed on Mar. 13,2019, which claims priority to European Patent Application No.18305278.6, filed on Mar. 14, 2018.

FIELD OF THE INVENTION

The present Invention relates to the field of signals processing toidentify and/or monitor physiological data of an individual (for examplein healthcare system) or general statement of an environment, apredetermined space (for example a room, a machine, a building) or anobject (for example in smart home system, environment monitoring system,fire prevention system or the like).

DESCRIPTION OF THE RELATED ART

It is now possible to acquire on, or in the vicinity of, an individualor object, a plurality of signals using multiple and/or various sensors.For example, it is possible with these sensors to obtain signalrepresentative of electrocardiogram (ECG), electroencephalogram (EEG),respiration, blood pressure, presence of metabolite, temperature,individual physical activity or the like. Similarly, some of thesesensors allow to obtain signal representative of room temperature,hygrometry, pH, heavy metal detection, humidity, air pressure, airquality, ambient light or the like.

According to one example, monitoring the signals delivered by thesesensors allows determining individual specific physiological conditionthat might be impaired. For example, when an individual is having aseizure, specific signal features appear on the signals corresponding tothe electrocardiogram (ECG) or to respiration.

According to another example, monitoring the signals delivered by smokedetector and temperature sensor from a predetermined space (e.g., aroom) allows preventing fire occurrence.

U.S. Pat. No. 9,339,195 discloses a method in which seizure symptoms aredetected. In this method, a sensor module receives plurality of isolatedsignals from plurality of sensors and a feature detection module detectsplurality of isolated predefined features in said plurality isolatedsignals that are associated with seizure symptoms. The solution of thisdocument is restricted to the detection of seizure symptoms, it requiresa large amount of computational power and it is limited to theprocessing of isolated and independent signals. This existing techniquedoes not correlate the various isolated signals and is thereforeassociated with a higher false positive rate in the detection of theseizure state.

Thus, there is a need for methods which are able to process multipleindependent signals, wherein each of which can be of different scale,unit or system of measurement, by combining them in such a way that itbecomes possible to detect occurrence and/or co-occurrence of anymodification in the incoming signals. Hence the obtained combinedprocessed data will provide more precise high-level information allowingto predict, identify or monitor individual or environment evolution andto alert in real-time in case of degradation.

SUMMARY OF THE INVENTION

The present Invention concerns a method for processing at least twosignals produced by sensors, preferably by at least two sensors, themethod comprising:

-   -   (i) receiving from sensors at least two signals, wherein at        least one of said at least two signals is a temporal signal,    -   (ii) if the received temporal signal is not an asynchronous        signal, the received temporal signal is converted into an        asynchronous temporal signal comprising the events for said        temporal signal, the said events being representative of each        change of the said temporal signal,    -   (iii) analyzing each of said asynchronous signals received from        one sensor, received in (i) and/or converted in (ii), and        providing an activity profile of the said analyzed asynchronous        signal, the activity profile comprising at least an activity        value that varies as a function of the time (t) that has passed        since the most recent event among the successive events of the        said asynchronous signal,    -   (iv) at a given time t:        -   a. determining a temporal context (t TC), said temporal            context being defined as a set of activity profiles at said            given time t of each of the said asynchronous signals,        -   b. identifying a meta-event (t ME) by associating the said            temporal context determined in step (a) with at least one            temporal reference context selected from among at least two            predefined reference temporal contexts,        -   c. determining a meta-context (t MC) by determining the            degree of correlation among the different meta-events            identified in step (b),        -   d. identifying a reference meta-context (t refMC) by            association said meta-context determined in step (c) with at            least one reference meta-context selected from at least two            predefined reference meta-contexts.

According to preferred embodiment, step (iii) consists in analyzing eachof said asynchronous signals received from one sensor, received in (i)and/or converted in (ii), and providing an activity profile of the saidanalyzed asynchronous signal or sensor, the activity profile comprisingat least an activity value that decreases as a function of the time (t)that has passed since the most recent event among the successive eventsof the said asynchronous signal.

According to special embodiment, the said set of activity profileincludes the activity profile of the closest events in time and/orspace.

According to special embodiment, all the received signals from sensorsare temporal signals.

According to special embodiment, all the received temporal signals areasynchronous signals.

According to special embodiment, all the received temporal signals areasynchronous signals and step (ii) is absent.

The following terms or definitions are only provided to assist inunderstanding the present invention. These definitions should not beinterpreted as having ordinary skill in the range of less thanunderstands the art.

“Event” designates occurrence triggered by a stimulus exceedingtransition state.

“Event signal” designates any asynchronous signal consisting solely of asequence of events

“Asynchronous signal” consists in temporal signal characterized by asequence of asynchronous events (called “event signal”). Moreparticularly it designates any temporal signal whose values are adjustedor updated non-periodically i.e the time between two value adjustmentsmay vary.

All these terms are well known in the art.

“Asynchronous signal”, “event signal”, “transformed temporal signal intoa sequence of asynchronous events” are all “event based signals”.

Step (ii) is optional and concerns embodiment of the method where thereceived temporal signal is not an asynchronous signal. Step (ii)consists in transforming the received temporal signal into a sequence ofasynchronous events (called “event signal”) that represents changes inthe signal capture by the sensor at the time they occur. The followingsteps of the method comprises the analysis of said event signal usingactivity profiles as events are received with the asynchronous signalobtained from temporal signal.

The activity profile comprises, for each sensor or for each asynchronoussignal (said asynchronous signals being received directly from sensor in(i) and/or being converted in (ii)), at least an activity value thatvaries as a function of the time that has passed since the most recentevent among the successive events from said sensor. According topreferred embodiment, the activity profile comprises, for each sensor orfor each asynchronous signal, at least an activity value that decreasesas a function of the time that has passed since the most recent eventamong the successive events from said sensor

Therefore the “activity profile” of a sensor or of an asynchronoussignal can be seen as a curve as a function of time of which the valuerepresents, at least, the time of the last event received for thissensor or for this asynchronous signal. It has been observed that themorphology of the activity profiles denotes the presence of certainbasic patterns in the signal acquired by the sensor.

The “event signal” can be the set of events coming from a given sensoror a subset of these events (space subset: limited to certain timesubset, limited to a given period of time).

According to special embodiment, the activity value of the activityprofile varies exponentially as a function of the time that has passedsince the most recent event among the successive events generated fromone sensor.

According to preferred embodiment, the activity value of the activityprofile decreases exponentially as a function of the time that haspassed since the most recent event among the successive events generatedfrom one sensor.

In a particular embodiment, the activity profile comprises, for eachsensor or for each asynchronous signal, at least an activity value thatvaries, preferably decreases, as a function of the time that has passedsince an event prior to the most recent event among the successiveevents from said sensor.

According to the present Invention, a reference meta-context determinedin step (c) can be correlated to a specific physiological condition ofan individual, for example a clinical state or medical condition.Alternatively, a reference meta-context can be correlated to a specificstatement of an environment, a predetermined space or an object.

According to one special embodiment, the at least two signals arereceived in step (i) through a communication interface having atransmitter/receiver for transmitting and receiving said signals.According to special embodiment, said communication interface is workingvia a wired or wireless link.

The use of event based signals allows performing theassociation/correlation steps on more a limited set of information. Thispermits processing more signals generated by sensors.

According to preferred embodiment, the association in steps b or/and dis performed according to method of the art. According to one specialembodiment, said association is made by calculating the distance betweenthe considered context (temporal or/and meta-context) and correspondingreference context (i.e. reference temporal context or/and referencemeta-context) belonging to a group of predetermined reference contexts,wherein said distance is a minimum distance.

According to another embodiment, said association step is made bymethods from the field of machine learning such as Spiking NeuralNetworks (SNN), Multilayer Perceptrons (MLP) or an Auto-encoder (AE).Spiking Neural Networks may be preferably used for continuousidentifications as these networks output an identified reference contextis a detection threshold has been reached.

According to one special embodiment, for a same signal, a plurality ofdetections is carried out. Each predetermined feature to be detected hasits own event signal. This allows performing a classification within thetemporal signal, and improves the precision of the identification of areference context.

According to preferred embodiment, at least two of the said receivedsignals are of different scale, unit or system of measurement and/or areoriginating from different sensor types.

According to preferred embodiment, said received signals are selected inthe group consisting of signals representative of electrocardiogram(ECG), electroencephalogram (EEG), respiration, blood pressure, bodytemperature, individual physical activity or the like.

Alternatively, said received signals are selected in the groupconsisting of signals representative of room temperature, hygrometry,pH, heavy metal detection, humidity, air pressure, air quality, ambientlight or the like.

According to preferred embodiment, the method of the Invention comprisesat least two temporal signals, preferably at least three temporalsignals, event more preferably at least five temporal signals.

According to special embodiment, reference context (i.e. referencetemporal context or/and reference meta-context) is associated with anevent signal, and, when a reference context is identified, the eventsignal associated with this reference context is adjusted at a value andvaries, preferably decreases, subsequently over time. According tospecial embodiment, each reference context is associated with an eventsignal, and, when a reference context is identified, the event signalassociated with this reference context is adjusted at a value andvaries, preferably decreases, subsequently over time.

It should be noted that the event signals associated with a referencesignal and the event signals associated with predetermined signalfeatures mentioned in the present application have similar properties.For example, the value at which an event signal associated with areference context is adjusted, and the duration of the variation,preferably the decrease, of such an event signal, may both be adjustedby the skilled person according to what the reference contextrepresents.

The use of event signals associated with reference context according tothe present Invention allows tracking each detection of a referencecontext in a manner which facilitates further processing.

According to another special embodiment, the method of the Inventionfurther comprises the following step:

-   -   (v) at a given time t+n:        -   a′. determination of a temporal context (t+n TC), said            context being defined as a set of activity profiles at said            given time t+n of the said asynchronous signals,        -   b′. identifying a meta-event (t+n ME) by associating each of            said temporal context determined in step (a′) with at least            one temporal reference context selected from among at least            two predefined reference temporal contexts,        -   c′. determining of a meta-context (t+n MC) by determining            the degree of correlation among the different meta-events            identified in step (b′) and arising from the said at least            two signals,        -   d′. identifying a reference meta-context (t+n refMC) by            association of said meta-context determined in step (c′)            with at least one reference meta-context selected from at            least two predefined reference meta-contexts.

According to special embodiment, t=t+n (n=0).

According to another special embodiment, t is different from t+n.

According to special embodiment n is 1.

According to special embodiment, reference context (i.e. referencetemporal context or/and reference meta-context) is associated with anevent signal, and, when a reference context is identified, the eventsignal associated with this reference context is adjusted at a value andvaries, preferably decreases, subsequently over time. According tospecial embodiment, each reference context is associated with an eventsignal, and, when a reference context is identified, the event signalassociated with this reference context is adjusted at a value andvaries, preferably decreases, subsequently over time.

According to an embodiment, each reference meta-context identified instep (d′) is associated with an event signal, and, when a referencemeta-context is identified in step (d′), the event signal associatedwith this reference meta-context is adjusted at a value and varies,preferably decreases, subsequently over time.

The method of the Invention may be recursive. Subsequent meta-contextcan be deduced from one or more event signals based on one or moresubsequent reference meta-context.

It should be noted that a classification based on a meta-context mayonly be performed if the required classifications identifying referencecontexts have been carried out. The skilled person will be able todetermine when each classification should be performed.

According to an embodiment, the identification of a first referencemeta-context or second reference meta-context is preceded by theidentification of at least one supplementary context at a third giventime, the method further comprising a classification step of thereference meta-context and of the third reference meta-context toidentify a reference signature of a period comprising the third giventime and the given time or the second given time.

This embodiment allows using different reference meta-context which havebeen identified, to identify a state which is only visible on thisperiod comprising the third given time and the first given time or thesecond given time.

The skilled person will know which classification method should be usedto identify the signature.

According to an embodiment, the reference signature is associated withan event signal and, when a reference signature is identified, the eventsignal associated with this reference signature is adjusted at a valueand varies, preferably decreases, subsequently over time.

In other words, in a manner which is similar to reference contexts,signatures are associated with event signals. It is possible to usethese event signals for a context which may be determined after asignature has been identified, for example for classification purposes.

According to an embodiment, a meta-context is determined as alsocomprising the value of an indicator or of received data.

This indicator may also be designated by the person skilled in the artas a flag, for example a binary value which may be set at zero or one toindicate something.

By way of example, the user may set this indicator at “1” (or “0”) toindicate a specific condition which may not be observable by a sensor,and this indicator will be taken into account in the classification stepbecause it is part of a (second) context.

Alternatively, received data may be used. This data may be anyadditional information which is not acquired using a sensor or definedby an indicator.

According to an embodiment, performing a classification to identify areference meta-context or a second reference meta-context or a signaturefurther comprises identifying a future reference meta-context orsignature (if the classification provides signatures) at a future giventime.

In other words, classification that identifies a meta contexte whichcontribute to the definition of a prediction on future state (see FIG.3A).

According to an embodiment, the method comprises delivering aprobability value associated with this future reference meta-context.For example, this probability value indicates the probability that thisfuture reference meta-context or signature is reached.

According to an embodiment, the method further comprises performing afurther action taking into account an identified reference meta-contextand/or an identified reference signature.

By way of example, this action may be triggering an alarm, sending amessage, or operating a closed loop device (such as a syringe pump).

According to an embodiment, the at least one temporal signal is acquiredon an individual and relates to physiological data of the individual.

The invention further relates to a system for processing signals whereinsaid system comprises one or more sensors that are able to generatesignals, wherein at least one of said signal is temporal signal, and aprocessing unit that implements the method of the Invention.

According to one special embodiment, the one or more sensors arearranged on an item configured to be worn by an individual.

In this embodiment, the one or more sensors acquire physiological dataof the individual.

By way of example, this item may be clothing such as a shirt or at-shirt.

By way of example, this item may be an electronic patch positioned onthe body of the individual.

The method of the Invention may be implemented by a processor of theseparate device. This separate device may be a smartphone, a smartwatch,a tablet, etc. Communication between the sensors and the separate deviceand may be wired (for example via USB: Universal Serial Bus) or wireless(for example via Bluetooth).

In one particular embodiment, the steps of the as defined above aredetermined by computer program instructions.

Consequently, the invention is also directed to a computer program forexecuting the steps of a method as described above when this program isexecuted by a computer.

The invention further relates to a computer program, implementing all ora portion of the method described hereinabove, installed on pre-existingequipment.

The invention further relates to a non-transitory computer-readablemedium on which is stored a computer program comprising instructions forthe implementation of the method of the Invention, when this program isexecuted by a processor.

This program can use any programming language (for example, anobject-oriented language or other), and by in the form of aninterpretable source code, object code or a code intermediate betweensource code and object code, such as a partially compiled form, anentirely compiled code, or any other desirable form.

The information medium can be any entity or device capable of storingthe program. For example, the medium can include storage means such as aROM, for example a CD ROM or a microelectronic circuit ROM, or magneticstorage means, for example a diskette (floppy disk) or a hard disk.

Alternatively, the information medium can be an integrated circuit inwhich the program is incorporated, the circuit being adapted to executethe method in question or to be used in its execution.

In this embodiment, the device comprises a computer, comprising a memoryfor storing instructions that allow for the implementation of themethod, the data concerning the stream of events received, and temporarydata for performing the various steps of the method such as describedhereinabove.

The computer further comprises a circuit. This circuit can be, forexample:

a processor able to interpret instructions in the form of a computerprogram, or

an electronic card of which the steps of the method of the invention aredescribed in the silicon, or

a programmable electronic chip such as a FPGA chip (Field-ProgrammableGate Array).

This computer comprises an input interface for receiving signals fromsensors. Finally, the computer can comprise, in order to allow for easyinteraction with a user, a screen and a keyboard. Of course, thekeyboard is optional, in particular in the framework of a computer thathas the form of a touch-sensitive tablet, for example.

BRIEF DESCRIPTION OF THE DRAWINGS

How the present disclosure may be put into effect will now be describedby way of example with reference to the appended drawings, in which:

FIG. 1 is a schematic representation of the steps of the method forprocessing physiological data according to an example,

FIG. 2 illustrates the processing of a single temporal signal is aschematic representation of the steps of a method in which secondcontexts are determined,

FIGS. 3A and 3B are a schematic representations of the steps of a methodin which second contexts are determined,

FIG. 4 is a schematic representation of a system according to anexample,

FIG. 5 is an schematic representation of a system comprising a clothingitem,

FIG. 6 is a schematic representation of a system comprising analternative clothing item,

FIG. 7 is a schematic representation of sleep apnea detection using thefusion of ECG, respiratory signal, oximeter signal and EEG.

DESCRIPTION OF THE EMBODIMENTS

FIG. 1 illustrates the steps of the method for processing physiologicaldata of an individual.

The invention is however not limited to processing physiological data ofan individual and may also apply to signals which relate to an object ora room or a building.

In a first step S10, a step of collecting physiological data is carriedout. This step may be carried out by receiving physiological datathrough a communication interface (for example through a wired orwireless interface). In other words, the method can be performedremotely with respect to the individual.

In the present example, the physiological data includes a first temporalsignal, a second temporal signal, and an indicator or flag having thevalue “1”.

The temporal signals may have been acquired on the individual and may beelectrical signals of the analog type (i.e. continuous signals) ordigital signals (i.e. sampled signals). By way of example, the firstsensed signal may be an ECG signal, and the second sensed signal may bethe output of a sensor which monitors the respiration of the individual.

It should be noted that in the present application, temporal signals aresignals which have a value which vary over time.

The indicator having the value “1” may be, for example, an indicatorwhich indicates a specific condition of the individual. For example, theindicator may indicate that the individual has undergone a specificsurgery, or that the individual has taken drugs. Also for example, suchan indicator may be acquired through a command of the user received instep S10.

It should be noted that step S10 may be carried out in a continuousmanner, or quasi-continuous manner in which when new samples have beenacquired for the temporal signals, these new samples are collected.

Detection steps S21, S22, and S23 are carried out once the temporalsignals and the indicator have been collected or continuously along thecollection of the temporal signals.

In step S21, a predetermined signal feature designated by “A” isdetected in the first temporal signal. Feature “A”, may be, for example,the first temporal signal reaching a predetermined value.

Each detection of feature “A” is followed by an adjusting step (stepS31) in which a time signal associated with the feature “A” called eventsignal A(t) is adjusted at a value (for example 1). As can be seen onthe figure, feature “A” is detected twice in the portion of time whichis shown. The event signal A(t) is adjusted twice at the same value andsubsequently. A(t) decreases over time in a linear manner with apredefined slope. This slope is chosen to illustrate the duration duringwhich feature “A” remains relevant.

In step S22, a predetermined signal feature designated by “B” isdetected in the first temporal signal. Feature “B”, may be, for example,the width of a peak in the first temporal signal.

Each detection of feature “B” is followed by an adjusting step (stepS32) in which a time signal associated with the feature “B” called eventsignal B(t) is adjusted at a value (for example 1). As can be seen onthe figure, feature “B” is detected once in the portion of time which isshown. After having been adjusted at a value (for example 1), B(t)decreases over time in a linear manner with a predefined slope. As canbe seen on the figure, this slope is not as steep as the slope shown forevent signal A(t). Thus, feature “B” may have an impact on theindividual which remains relevant for a longer time.

In step S23, a predetermined signal feature designated by “C” isdetected in the second temporal signal. Feature “C”, may be, forexample, the second temporal signal reaching a predetermined value.

Each detection of feature “C” is followed by an adjusting step (stepS33) in which a time signal associated with the feature “C” called eventsignal C(t) is adjusted at a value (for example 1). As can be seen onthe figure, feature “C” is detected once in the portion of time which isshown. After having been adjusted at a value (for example 1), C(t)decreases over time in a linear manner with a predefined slope.

At a given time, in order to detect that the individual is in aparticular state, it is possible to use the event signals and the flag.To this end, it is possible to use all the event signals which have beenpreviously adjusted or a portion of the event signals.

Additionally, for a given detection, it is possible to adjust further anevent signal, for example by applying a coefficient which is less thanone to an event signal which is less significant for a specificdetection. For example, there may be, for each type of detection, ahierarchy between the event signals which is embodied by coefficientsapplied to event signals. The skilled person will be able to determinethese coefficients, for example during calibration steps.

Step S40 is carried out at a given time designated by t0. By way ofexample, step S40 may be performed at regular intervals, for exampleevery minute. In this step, a context C is determined as comprising thevalue of each event signal at t0 and the flag at “1”:C=(A(t0);B(t0);C(t0);“1”)

In this example, C is a vector comprising 4 components.

This context can then be used in a classification step S50 in which areference context is identified. In this example, a group of referencecontexts has been defined preliminarily, for example in a calibrationstep. Each reference context may be associated with a specific state ofthe individual.

Classification step S50 may be performed by means of a distancecalculation (i.e. the distance between the context C and each referencecontext; the person skilled in the art will be able to determine whichmathematical distance should be used), or also by means of methods fromthe field of machine learning such as Spiking Neural Networks (SNN),Multilayer Perceptrons (MLP) or using Auto-Encoders (AE).

A reference context may then be identified.

FIG. 2 illustrates the processing, according to an example, of a singlesensed signal 300, which is an ECG signal. This signal 300 comprises aplurality of QRS complexes well known to the person skilled in the art.The first QRS complex shown in the signal is designated by reference 301it is also shown in more detail on the right of signal 300.

In the illustrated example, predetermined signal features relate to thetemporal signal reaching predefined levels L1, L2, and L3 at specificinstants of a duration T.

These predetermined features have been observed to allow theidentification of various states of the individual. Every time thesefeatures are detected, a peak (other shapes of signal may be used) isgenerated on a signal 302. This signal 302 illustrates the detection ofpredefined features.

On signal 302, when a predefined level is reached by an increasingsignal, a positive peak is generated. When a predefined level is reachedby a decreasing signal, a negative peak is generated.

From the order of these peaks, it is possible to know which predefinedfeature has been detected.

Thus, it is possible to adjust corresponding event signals F1, F2, F3,F4, and F5 at a value every time the corresponding predetermined featureis detected. Each event signal F1 to F5 decreases right after the signalhas been adjusted at a value.

At a given time t0, a context is determined as comprising all the valueof event signals F1 to F5.

The context 304 is obtained. On the figure, this context 304 isrepresented in the form of a radar chart.

It is possible to identify a reference context using a classificationmethod using the context 304 as input. For example, a distance betweencontext 304 and a reference contexts may be used for the classification.

The identified reference context may belong to a group of referencecontexts 305 comprising notably reference contexts 305A, 305B, 305Cwhich have been represented on the figure (other reference contexts havenot been represented for the sake of conciseness). Reference context305B and the context 304 are graphically close and this referencecontext should be identified.

The identified reference context 305B, or a value corresponding to thisreference context, is inputted to a classifier 306 which performs afurther classification.

For example, at another given time t0′, which may precede t0, it ispossible to determine a context 307. It is possible to identify anotherreference context from the group of reference contexts. In this example,reference context 308 is identified.

Reference context 308, or a value corresponding to this referencecontext, is also inputted to the classifier 306.

For example, the classifier 306 may be able to detect 5 differentsignatures each designated by letters:

N: Normal state;

S: Supraventicular premature beat;

V: Premature Ventricular contraction;

F: Fibrillation; and

O: Other, unclassified events.

These signatures may each be associated with an event signal. Also, thereference context N: Normal state may preferably not be associated witha second event signal in order to limit the quantity of data to begenerated.

Preferably, reference context 306 uses a Spiking Neural Network, whichmay only output a signature if a predefined detection threshold has beenreached.

FIG. 3A is an example of method in which second contexts are determined.

On this figure, different temporal signals are represented. Thesesignals have been acquired on an individual. A first temporal signal 201illustrates the respiration of the individual, a second temporal signal202 illustrates the ECG of the individual, a third temporal signal 203illustrates the temperature of the user. These temporal signals are allof different types.

For the temporal signal 201, by applying a method similar to the onedisclosed in reference to FIG. 1 to only this temporal signal, twopossible reference contexts may be identified. When one of these tworeference contexts is identified, an event signal E11(t) correspondingto the reference context which has been identified is adjusted at avalue (for example 1). Subsequently, the event signal E11(t) decreasesover time in a manner which is analogous to the event signals describedin reference to FIG. 1.

Similarly, when the other reference context is identified, an eventsignal E12(t) corresponding to this other reference context is adjustedat a value (for example 1). Subsequently, the event signal E12(t)decreases over time.

For the second temporal signal 202, three event signals corresponding tothree different reference contexts are elaborated: E21(t), E22(t), andE23(t). It should be noted that alternatively one or more of these eventsignals may be associated with a signature which has been identified, asdisclosed in reference to FIG. 2.

For the third temporal signal 203 one event signal corresponding iselaborated: E31(t). This event signal may be elaborated on the basis ofthe detection of a predetermined signal feature of signal 203.

From these event signals, it is possible to detect a state in which theindividual is and which can be identified because this state has beenobserved to be associated with a plurality of specific contexts beingidentified within a timeframe: this implies that observing thecorresponding event signals allows detecting this state.

For example, this detection may be performed regularly, for exampleevery 24 h.

In order to be able to detect this state at a given time t1, a context Cis determined as:C=(E11(t1);E12(t1);E21(t1);E22(t1);E23(t1);E31(t1))

C is a vector of 6 components in this example.

This context C can then be used in a classification step S80 in which asecond reference context is identified. In this example, a group ofsecond reference contexts has been defined preliminarily, for example ina calibration step. Each second reference context may be associated witha specific state of the individual.

Classification step S80 may be performed by means of a distancecalculation (i.e. the distance between the context C and each secondreference context; the person skilled in the art will be able todetermine which mathematical distance should be used), or also by meansof methods from the field of machine learning such as Spiking NeuralNetworks (SNN) or Multilayer Perceptrons (MLP).

As shown on FIG. 2, the classification step outputs both a secondreference context which has been identified, and a prediction.

This prediction comprises a second reference context which may beidentified at t1+Δt, wherein Δt is a predefined duration. Additionally,the prediction may be associated with a probability value.

The skilled person will be able to select the appropriate classificationmethod to be used to also output a probability value. This probabilityvalue may indicate the probability this second reference context to beidentified.

It should be noted that event signals may be elaborated on the basis ofan identification of a context or on the basis of an identification of asecond context.

FIG. 3B is an alternative implementation of the method shown on FIG. 3A,in which the event signals E11(t) and E12(t) associated with temporalsignal 201 are processed (step S100) to obtain a single valuerepresenting the state of these event signals at t1.

The event signals E21(t), E22(t) and E23(t) associated with temporalsignal 202 are processed (Step S101) to obtain a single valuerepresenting the state of these event signals at t1.

Thus, this simplifies the determination of the context and theclassification of step S80

As shown on the figure, a radar diagram showing the context has beenrepresented. Each component is associated with a different physiologicalphenomenon.

FIG. 4 shows an example of system according to an example. This systemmay be configured to perform the various embodiments described inreference to FIGS. 1 to 3.

The system 400 comprises a device 401 which communicates with twoexternal (with respect to the device 401) sensors 402 and 403.Communication may be obtained using a communication interface 404 of thedevice 401. For example, communication interface may be a wiredcommunication interface such as a USB interface. Sensors 402 and 403 areconfigured to acquire temporal signals on an individual which constitutephysiological data of the individual.

The device 401 further comprises a processor 405 which processes thetemporal signals, and a non-volatile memory 406.

This non-volatile memory 406 comprises referenced contexts 407, and aset of instructions 408, 408, 410, and 411. When executed by theprocessor 405, these instructions and the processor 405 form modules ofthe device 401:

Instructions 408, when executed by processor 405, perform detecting, inthe temporal signals acquired by sensor 402 and 403, at least onepredetermined signal feature. Instructions 408 and the processor 405form a detecting module that detects, in the temporal signals comprisedsignals, at least one predetermined signal feature.

Instructions 409, when executed by processor 405, perform adjusting at avalue a time signal associated with the at least one predeterminedsignal feature called event signal, when the at least one predeterminedsignal feature is detected, the event signal subsequently decreasingover time. Instructions 409 and the processor 405 form an adjustingmodule that adjusts at a value a time signal associated with the atleast one predetermined signal feature called event signal, when the atleast one predetermined signal feature is detected, the event signalsubsequently decreasing over time.

Instructions 410, when executed by processor 405, perform, at a giventime, determining a context as comprising at least the value of theevent signal at this given time. Instructions 410 and the processor 405form a determining module that determines, at a given time, a context ascomprising at least the value of the event signal at this given time.

Instructions 411, when executed by processor 405, perform aclassification of the context so as to identify a reference context(from the reference contexts 407). Instructions 411 and the processor405 form a classification module that classifies, at a given time, thecontext so as to identify a reference context.

On FIG. 5, a system 500 has been represented. This system comprises adevice 501 which may be a smartphone and which is analogous to thedevice 401 described in reference to FIG. 4.

In this example, three sensors are shown as embedded in an item ofclothing: a t-shirt. These sensors are referenced 503, 504, and 505. Thethree sensors 503 to 505 are connected to a communication module 506which communicates wirelessly with the device 501 through communicationlink L1. The wireless communication may be performed using Bluetooth orany other appropriate wireless communication protocol.

As shown on the figure, the screen of the device 501 may display analert message according to an identified reference context.

On FIG. 6, a system 600 has been represented. The system 600 may be inthe form of an item of clothing: an armband.

This armband comprises a device 601 which is embedded in the armband andwhich is analogous to the device 401 described in reference to FIG. 4.The armband is also equipped with sensors 602, 604, and 604.

As can be understood from the above examples, the invention may beimplemented in a compact manner.

The use of event signals also allows obtaining real-time results (forexample contexts may be identified every second).

Also, the use of (second) event signals originating from different typesof temporal signals allows identifying complex states of an individual.Thus, it is possible to improve the detection of states which may bedetrimental, for example to an individual, so as to proactively protectthe individual or to alert the individual.

FIG. 7 illustrates the use of the data fusion method of invention to amultiparametric sleep apnea detector using the fusion of ECG,respiratory signal, oximeter signal and EEG.

Data fusion is the process of integrating multiple data sources toproduce more consistent, accurate, and useful information than thatprovided by any individual data source. Features extraction is appliedusing method of the invention on each isolated signal from each sensor.The features are then combined to detect specific physiologicalcondition (see FIG. 7).

Sleep apnea is often diagnosed using Polysomnography (PSG) methodconsisting in monitoring multiple physiological signals during overnightsleep. That is why the detection of this pathology is a good example toillustrate data fusion method of the invention. Sleep apnea is a sleepdisorder characterized by pauses in breathing or periods of shallowbreathing during sleep. Each pause can last from a few seconds to a fewminutes and can happen many times a night. There are three forms ofsleep apnea: obstructive (OSA), the most common form, central (CSA), anda combination of the two called mixed. The disorder disrupts normalsleep and it can lead to hypersomnolence, neurocognitive dysfunction,cardiovascular disease, metabolic dysfunction and respiratory failure.Sleep apnea is also a common pathology in epileptic patients, and canlead to death. Monitored signals usually include electroencephalography(EEG), airflow, thoracic or abdominal respiratory effort signals andblood oxygen saturation. The analysis of PSG requires dedicatedpersonnel and is very time consuming. Moreover it involves inter-raterreliability variation in scorers. An automatic sleep apnea detection istherefore needed.

Performance Metrics:

Sleep apnea detectors are evaluated in terms of Se, SP, overall accuracy(ACC), and F1-score. These metrics relies on the number of truepositives (TP: number of cases correctly identified as sleep apnea),true negatives (TN: number of cases correctly identified as non sleepapnea), false positives (FP: number of cases incorrectly identified assleep apnea), and false negatives (FN: number of cases incorrectlyidentified as non sleep apnea) and are calculated as follow:

${{Se} = \frac{TP}{{TP} + {FN}}},{{Sp} = \frac{TN}{{TN} + {FP}}}$${{F\; 1{\_ score}} = \frac{2*{Se}*{Sp}}{{Se} + {Sp}}},{{ACC} = \frac{{TP} + {TN}}{{TP} + {FP} + {TN} + {FN}}}$Sensitivity (Se) refers to the ability to correctly detect sleep apnea,and specificity (Sp) evaluates the ability to correctly reject patientswith no sleep apnea.Results:Data are 35 recordings of 7 hours to nearly 10 hours. Each recordingincludes an ECG signal, and only 4 recordings include also chest andabdominal respiratory eort signals. The presence or absence of apnea isindicated for each minute of each recording. Only OSA apneas are presentin the dataset. Only recordings containing both ECG and respiratorysignals are used, in order to evaluate the interest of data fusion.Apnea detection performance using a single parameter is compared withthe detection using multiple parameters, by applying our method onrespiratory signals only, and then on both signals (ECG+ respiratorysignals). An increase of more than 15% in F1-score is obtained thanks todata fusion. However the size of the available dataset limits thelearning and the results.

Se Sp F1-score Accuracy Resp. only 78% 71%  74% 77% Resp. + ECG 89% 90%89.5% 89%The performance of the method was validated on another dataset: theMIT-BIH Polysomnographic Database, which is a collection of recordingsof multiple physiologic signals during sleep. The database contains over80 hours recordings of four, six, and seven-channel. Only 4 recordingsinclude an ECG signal, an EEG signal, nasal, chest and abdominalrespiratory eort signals, and an earlobe oximeter signal (SO2). Sleepstages and presence or absence of different types of apnea are indicatedfor each 30 s of each recording. The dataset contains different types ofapnea, OSA and CSA, and different types of sleep stages (1, 2, 3, 4 andawake). The classification does not make any distinction between thedifferent types of apnea, and portions of signal are classified intoeither “Apnea” or “Non-apnea”. Signals are classified using only therespiratory effort signals. A F1-score of 81% is obtained. By combiningthe information of the ECG and the respiration a F1-score of 84% isobtained. The results are improved by first classifying portions ofsignals into different sleep stages. EEG is used to classify signalsinto 4 categories: “sleep stage 1”, “sleep stage 2”, “sleep stage 3 and4”, and “awake”. Then ECG, respiratory, and oximeter signals areanalyzed to classify recordings into “Apnea” or “Non-apnea”. A F1-scoreof 94.4% is obtained. The results of classification keep increasing whenwe add more physiological signals.

Se Sp F1-score Resp. only 74.5%  91% 81% +ECG 94% 76% 84% +S0₂ + EEG 91%98% 94.4% 

CONCLUSION

Sleep apnea detection has been validated with two multiparametricdatabase: the MIT-BIH Polysomnographic Database and the dataset from theCinC Challenge 2000. These databases contain multiple physiologicsignals, and enable the evaluation of data fusion. Recordings consist ofECG, EEG, respiratory signals, and earlobe oximeter signal. By combiningfeatures from the ECG and from the respiratory signal, an increase of15% in F1 score has been obtained, compared with features fromrespiration only, for the CinC Challenge 2000 dataset. The recordingsfrom the MIT-BIH Polysomnographic Database present different types ofapnea, and different sleep stages. Portions of signals were firstclassified into sleep stages, using the EEG information. Then method ofthe invention was applied on the ECG, respiratory and oximeter signals.A sensitivity (Se) of 91% and a specificity (Sp) of 98% were obtained.In conclusion, data fusion improves the results of apnea detection.Monitoring multiple physiological signals can lead to a better detectionof different pathologies.

The invention claimed is:
 1. A method for processing at least twosignals produced by sensors, preferably by at least two sensors, themethod comprising: (i) receiving from sensors at least two signals,wherein at least one of said at least two signals is a temporal signal,(ii) if the received temporal signal is not an asynchronous signal, thereceived temporal signal is converted into an asynchronous temporalsignal comprising the events for said temporal signal, the said eventsbeing representative of each change of the said temporal signal, (iii)analyzing each of said asynchronous signals received from one sensor,received in (i) and/or converted in (ii), and providing an activityprofile of the said analyzed asynchronous signal, the activity profilecomprising at least an activity value that varies as a function of atime (t) that has passed since a most recent event among successiveevents of the said asynchronous signal, (iv) at a given first time: a.determining of a first temporal context (tTC), said first temporalcontext being defined as a set of activity profiles at said given firsttime of the said asynchronous signals, b. identifying a first meta-event(t ME) by associating the said first temporal context determined in step(a) with at least one temporal reference context selected from among atleast two predefined reference temporal contexts, c. determining a firstmeta-context (t refMC) by determining a degree of correlation among thedifferent first meta-events identified in step (b) and arising from thesaid at least two signals, d. identify a first reference meta-context byassociation said meta-context determined in step (c) with at least onereference meta-context selected from at least two predefined referencemeta-contexts.
 2. The method of claim 1 wherein step (iii) consists inanalyzing each of said asynchronous signals received from one sensor,received in (i) and/or converted in (ii), and providing an activityprofile of the said analyzed asynchronous signal or sensor, the activityprofile comprising at least an activity value that decreases as afunction of the time (t) that has passed since the most recent eventamong the successive events of the said asynchronous signal.
 3. Themethod of claim 1 wherein all the received signals from sensors aretemporal signals.
 4. The method of claim 1 wherein all the receivedtemporal signals are asynchronous signals and step (ii) is absent. 5.The method of claim 1, wherein a reference meta-context determined instep (c) can be correlated to a specific physiological condition of anindividual.
 6. The method claim 1, wherein at least two of the saidreceived signals are of different scale, unit or system of measurementand/or are originating from different sensor types.
 7. The method ofclaim 1, wherein said received signals are selected in a groupconsisting of signals representative of electrocardiogram (ECG),electroencephalogram (EEG), respiration, blood pressure, bodytemperature, individual physical activity or the like.
 8. The method ofclaim 1 further comprising at least two temporal signals.
 9. The methodof claim 1 wherein at least two of said at least two signals aretemporal signals.
 10. The method of claim 1, further comprising: (i) ata given time t+n: a′. determination of a temporal context (t+n TC), saidcontext being defined as a set of activity profiles at said given timet+n of the said asynchronous signals, b′. identifying a meta-event (t+nME) by associating each of said temporal context determined in step (a′)with at least one temporal reference context selected from among atleast two predefined reference temporal contexts, c′. determining of ameta-context (t+n MC) by determining the degree of correlation among thedifferent meta-events identified in step (b′) and arising from the saidat least two signals, d′. identifying a reference meta-context (t+nrefMC) by association of said meta-context determined in step (c′) withat least one reference meta-context selected from at least twopredefined reference meta-contexts.
 11. The method of claim 10 whereint=t+n (n=0).
 12. The method of claim 10 wherein t is different from t+n.13. The method of claim 1 wherein the said reference temporal contextor/and reference meta-context is associated with an event signal, and,when a reference context is identified, the event signal associated withthis reference context is adjusted at a value and varies, preferablydecreases, subsequently over time.
 14. The method of claim 1 wherein thesaid method is recursive.
 15. The method of claim 1 wherein theprediction comprises a second reference context which may be identifiedat t1+Δt with Δt is a predefined duration).
 16. The method of claim 15,wherein the prediction is associated with a probability value.
 17. Themethod of claim 15, wherein the prediction is associated with an alert.18. A method for processing at least two signals produced by sensors,preferably by at least two sensors, the method comprising: (i) receivingfrom sensors at least two signals, wherein at least one of said at leasttwo signals is a temporal signal, (ii) converting the received temporalsignal into an asynchronous temporal signal, based on determining thatthe received temporal signal is not an asynchronous signal, theconverted asynchronous temporal signal comprising events for saidtemporal signal, the said events being representative of each change ofthe said temporal signal, (iii) analyzing each of said asynchronoussignals received from one sensor, received in (i) and/or converted in(ii), and providing an activity profile of the said analyzedasynchronous signal, the activity profile comprising at least anactivity value that varies as a function of a time (t) that has passedsince a most recent event among successive events of the saidasynchronous signal, (iv) at a given first time: a. determining of afirst temporal context (tTC), said first temporal context being definedas a set of activity profiles at said given first time of the saidasynchronous signals, b. identifying a first meta-event (t ME) byassociating the said first temporal context determined in step (a) withat least one temporal reference context selected from among at least twopredefined reference temporal contexts, c. determining a firstmeta-context (t refMC) by determining a degree of correlation among thedifferent first meta-events identified in step (b) and arising from thesaid at least two signals, d. identify a first reference meta-context byassociation said meta-context determined in step (c) with at least onereference meta-context selected from at least two predefined referencemeta-contexts.